Dynamic GPU energy optimization for machine learning training workloads
GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …
machine learning models become increasingly larger, they require a longer time to train …
Flbench: A benchmark suite for federated learning
Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …
learning model from the data sets distributed on multiple devices–so-called an isolated data …
Hpc ai500 v2. 0: The methodology, tools, and metrics for benchmarking hpc ai systems
Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
[HTML][HTML] Call for establishing benchmark science and engineering
J Zhan - BenchCouncil Transactions on Benchmarks, Standards …, 2021 - Elsevier
Currently, there is no consistent benchmarking across multi-disciplines. Even no previous
work tries to relate different categories of benchmarks in multi-disciplines. This article …
work tries to relate different categories of benchmarks in multi-disciplines. This article …
Aibench scenario: Scenario-distilling ai benchmarking
Modern real-world application scenarios like Internet services consist of a diversity of AI and
non-AI modules with huge code sizes and long and complicated execution paths, which …
non-AI modules with huge code sizes and long and complicated execution paths, which …
AI-oriented workload allocation for cloud-edge computing
Different placement or collaboration policies in handling datasets and workloads across
cloud, edge, and user-end may substantially affect a cloud-edge computing environment's …
cloud, edge, and user-end may substantially affect a cloud-edge computing environment's …
A UCSD view on replication and reproducibility for CPS & IoT
Reproducibility and replicability (R&R) are important for research. Many communities are
beginning efforts to reward, incentivize, and highlight projects as a motive to adopt R&R …
beginning efforts to reward, incentivize, and highlight projects as a motive to adopt R&R …
Hpc ai500: Representative, repeatable and simple hpc ai benchmarking
Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
[HTML][HTML] Stars shine: The report of 2021 BenchCouncil awards
T Zhan, S Chen - BenchCouncil Transactions on Benchmarks, Standards …, 2021 - Elsevier
Stars shine: The report of 2021 BenchCouncil awards - ScienceDirect Skip to main contentSkip
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